Recently, autoencoders (AEs) have been adopted as a neural item-to-item recommender models [1]}, [2]}, [3]}, [4]}, [5]}. The goal of an AE is to minimize the reconstruction error for a set of user rating vectors:
\(\operatornamewithlimits{argmin}_{\theta }\sum _{u=1}^m {\mathcal {L} \left(\mathbf {r}_u, M(\mathbf {r}_u; \theta ) \right)} + \lambda \Omega (\theta ),\) 
